Enhancing MAUDE Database Utility by GPT-4 and Cause-Effect Visualization

Authors

Language

English

Publication Date

7-24-2024

Journal

Studies in Health Technology and Informatics

DOI

10.3233/SHTI240155

PMID

39049270

Abstract

The MAUDE database is a valuable public resource for understanding malfunctions and adverse events related to medical devices and health IT. However, its extensive data and complex structure pose challenges. To overcome this, we have developed an automated analytical pipeline using GPT-4, a cutting-edge large language model. This pipeline is intended to efficiently extract, categorize, and visualize safety events with minimal human annotation. In our analysis of 4,459 colonoscopy reports from MAUDE (2011-2021), the events were categorized into operational, human factor, and device-related. Ishikawa diagrams visualized a subset stored in a vector database for easy retrieval and comparison through a similarity search. This innovative approach streamlines access to vital safety insights, reducing the workload on human annotators, and holds promise to enhance the utility of the MAUDE database.

Keywords

Humans, Databases, Factual, Colonoscopy, Equipment Failure, Natural Language Processing, Patient Safety, GPT4, Ishikawa diagram, MAUDE, Patient safety events, colonoscopy

Published Open-Access

yes

Recommended Citation

, "Enhancing MAUDE Database Utility by GPT-4 and Cause-Effect Visualization" (2024). Faculty, Staff and Student Publications. 863.
https://digitalcommons.library.tmc.edu/uthshis_docs/863